Summary of Towards Foundation Models on Graphs: An Analysis on Cross-dataset Transfer Of Pretrained Gnns, by Fabrizio Frasca et al.
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
by Fabrizio Frasca, Fabian Jogl, Moshe Eliasof, Matan Ostrovsky, Carola-Bibiane Schönlieb, Thomas Gärtner, Haggai Maron
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of Graph Neural Networks (GNNs) across various datasets without relying on dataset-specific features or encodings. The authors propose an extension to a structural pretraining approach that captures feature information while remaining agnostic to these features. They evaluate the performance of pretrained GNNs on downstream tasks, considering different amounts of training samples and pretraining datasets. Preliminary results show that embeddings from pretrained models improve generalization when given sufficient downstream data points and are influenced by the quantity and properties of pretraining data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to understand graphs, like social networks or transportation systems. Graphs have different structures and features, but the authors want to know if a single AI model can be used across many different graphs without needing to learn new things each time. They develop an approach that captures general patterns in the data while ignoring specific details. The results show that this model can generalize well when given enough information from the graph it’s working with. However, the amount of improvement depends on how similar the training and test data are. |
Keywords
» Artificial intelligence » Generalization » Pretraining
